Volunteer Summary

Table

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.39) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.18 (95% CI [18.52, 19.84], t(134) = 56.74, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.70, 95% CI [-0.24, 1.63], t(134) = 1.46, p = 0.143; Std. beta = 0.33, 95% CI [-0.11, 0.77])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.08, 0.49], t(134) = -0.74, p = 0.458; Std. beta = -0.14, 95% CI [-0.51, 0.23])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.01, 95% CI [-1.17, 1.14], t(134) = -0.02, p = 0.985; Std. beta = -5.28e-03, 95% CI [-0.55, 0.54])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.03 (95% CI [10.50, 11.55], t(134) = 40.84, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.40, 95% CI [-0.34, 1.14], t(134) = 1.05, p = 0.293; Std. beta = 0.24, 95% CI [-0.21, 0.68])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.27, 0.79], t(134) = 0.97, p = 0.332; Std. beta = 0.16, 95% CI [-0.16, 0.47])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.19, 95% CI [-0.97, 0.59], t(134) = -0.49, p = 0.627; Std. beta = -0.12, 95% CI [-0.58, 0.35])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.75) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.26 (95% CI [42.98, 45.53], t(134) = 68.21, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.19, 95% CI [-0.59, 2.98], t(134) = 1.31, p = 0.191; Std. beta = 0.30, 95% CI [-0.15, 0.75])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.90, 1.04], t(134) = 0.14, p = 0.892; Std. beta = 0.02, 95% CI [-0.23, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-1.16, 1.74], t(134) = 0.39, p = 0.694; Std. beta = 0.07, 95% CI [-0.29, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 8.01e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.62 (95% CI [14.95, 16.28], t(134) = 46.28, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-0.84, 1.01], t(134) = 0.18, p = 0.858; Std. beta = 0.04, 95% CI [-0.41, 0.49])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.43, 0.81], t(134) = 0.60, p = 0.551; Std. beta = 0.09, 95% CI [-0.21, 0.39])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.66, 1.18], t(134) = 0.55, p = 0.582; Std. beta = 0.12, 95% CI [-0.32, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.33 (95% CI [11.89, 12.78], t(134) = 54.15, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.69, 0.57], t(134) = -0.18, p = 0.855; Std. beta = -0.04, 95% CI [-0.49, 0.41])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.53, 95% CI [-0.90, -0.17], t(134) = -2.87, p = 0.004; Std. beta = -0.38, 95% CI [-0.64, -0.12])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-0.01, 1.07], t(134) = 1.91, p = 0.056; Std. beta = 0.38, 95% CI [-9.71e-03, 0.76])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 9.78e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.33 (95% CI [10.87, 11.79], t(134) = 48.41, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.50, 0.79], t(134) = 0.43, p = 0.667; Std. beta = 0.10, 95% CI [-0.35, 0.55])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.63, 0.38], t(134) = -0.49, p = 0.624; Std. beta = -0.09, 95% CI [-0.43, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-0.47, 1.01], t(134) = 0.71, p = 0.478; Std. beta = 0.19, 95% CI [-0.33, 0.70])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.48) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.41 (95% CI [42.98, 45.84], t(134) = 60.74, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.11, 95% CI [-0.90, 3.13], t(134) = 1.08, p = 0.278; Std. beta = 0.24, 95% CI [-0.20, 0.68])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -1.39, 95% CI [-2.96, 0.18], t(134) = -1.73, p = 0.084; Std. beta = -0.30, 95% CI [-0.64, 0.04])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-2.22, 2.43], t(134) = 0.09, p = 0.932; Std. beta = 0.02, 95% CI [-0.48, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.51 (95% CI [24.21, 28.81], t(134) = 22.59, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -2.01, 95% CI [-5.25, 1.22], t(134) = -1.22, p = 0.222; Std. beta = -0.27, 95% CI [-0.71, 0.17])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 1.23, 95% CI [-0.97, 3.42], t(134) = 1.10, p = 0.273; Std. beta = 0.17, 95% CI [-0.13, 0.46])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.37, 95% CI [-4.63, 1.89], t(134) = -0.82, p = 0.410; Std. beta = -0.19, 95% CI [-0.63, 0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 45.10 (95% CI [41.67, 48.54], t(134) = 25.74, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -3.13, 95% CI [-7.95, 1.70], t(134) = -1.27, p = 0.204; Std. beta = -0.28, 95% CI [-0.72, 0.15])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 2.65, 95% CI [-0.56, 5.86], t(134) = 1.62, p = 0.105; Std. beta = 0.24, 95% CI [-0.05, 0.53])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.52, 95% CI [-6.29, 3.25], t(134) = -0.63, p = 0.532; Std. beta = -0.14, 95% CI [-0.57, 0.29])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.82 (95% CI [19.66, 21.98], t(134) = 35.31, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.88, 95% CI [-0.74, 2.50], t(134) = 1.06, p = 0.288; Std. beta = 0.24, 95% CI [-0.21, 0.69])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.05, 95% CI [-1.13, 1.23], t(134) = 0.08, p = 0.937; Std. beta = 0.01, 95% CI [-0.31, 0.34])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.39, 95% CI [-2.14, 1.36], t(134) = -0.44, p = 0.659; Std. beta = -0.11, 95% CI [-0.59, 0.38])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.21 (95% CI [12.32, 14.09], t(134) = 29.38, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.47, 95% CI [-0.77, 1.71], t(134) = 0.74, p = 0.457; Std. beta = 0.17, 95% CI [-0.28, 0.62])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-1.20, 0.52], t(134) = -0.78, p = 0.438; Std. beta = -0.12, 95% CI [-0.44, 0.19])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.53, 95% CI [-0.75, 1.80], t(134) = 0.81, p = 0.418; Std. beta = 0.19, 95% CI [-0.27, 0.65])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 1.06e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.54 (95% CI [19.83, 21.24], t(134) = 57.06, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-1.15, 0.83], t(134) = -0.32, p = 0.747; Std. beta = -0.07, 95% CI [-0.52, 0.37])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.04, 95% CI [-0.75, 0.67], t(134) = -0.10, p = 0.921; Std. beta = -0.02, 95% CI [-0.34, 0.30])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.84, 1.27], t(134) = 0.41, p = 0.685; Std. beta = 0.10, 95% CI [-0.38, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.31) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.46 (95% CI [4.16, 4.76], t(134) = 29.16, p < .001). Within this model:

• The effect of group [treatment] is statistically significant and positive (beta = 0.46, 95% CI [0.04, 0.88], t(134) = 2.16, p = 0.031; Std. beta = 0.48, 95% CI [0.04, 0.91])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.22, 0.54], t(134) = 0.83, p = 0.406; Std. beta = 0.17, 95% CI [-0.22, 0.56])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.89e-03, 95% CI [-0.56, 0.56], t(134) = -0.01, p = 0.992; Std. beta = -2.96e-03, 95% CI [-0.58, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.52) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.03 (95% CI [57.32, 60.73], t(134) = 67.97, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.65, 95% CI [-0.74, 4.04], t(134) = 1.35, p = 0.177; Std. beta = 0.31, 95% CI [-0.14, 0.76])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.93, 1.64], t(134) = -0.16, p = 0.871; Std. beta = -0.03, 95% CI [-0.36, 0.31])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.30, 95% CI [-2.35, 2.94], t(134) = 0.22, p = 0.826; Std. beta = 0.06, 95% CI [-0.44, 0.55])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.64 (95% CI [15.08, 16.20], t(134) = 54.40, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.12, 95% CI [-0.91, 0.68], t(134) = -0.29, p = 0.774; Std. beta = -0.06, 95% CI [-0.51, 0.38])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.36, 95% CI [-0.97, 0.26], t(134) = -1.14, p = 0.253; Std. beta = -0.20, 95% CI [-0.54, 0.14])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.69, 95% CI [-0.21, 1.60], t(134) = 1.50, p = 0.134; Std. beta = 0.39, 95% CI [-0.12, 0.90])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 5.82e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.44 (95% CI [11.98, 12.89], t(134) = 53.86, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.26, 95% CI [-0.90, 0.38], t(134) = -0.80, p = 0.421; Std. beta = -0.18, 95% CI [-0.62, 0.26])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.44, 0.41], t(134) = -0.07, p = 0.944; Std. beta = -0.01, 95% CI [-0.31, 0.29])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.42, 0.85], t(134) = 0.66, p = 0.512; Std. beta = 0.15, 95% CI [-0.29, 0.59])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 5.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.08 (95% CI [27.14, 29.01], t(134) = 58.67, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.38, 95% CI [-1.70, 0.94], t(134) = -0.56, p = 0.575; Std. beta = -0.13, 95% CI [-0.57, 0.32])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.35, 95% CI [-1.26, 0.56], t(134) = -0.75, p = 0.451; Std. beta = -0.12, 95% CI [-0.42, 0.19])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.92, 95% CI [-0.43, 2.27], t(134) = 1.33, p = 0.183; Std. beta = 0.31, 95% CI [-0.15, 0.76])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 1.28e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.95 (95% CI [13.57, 16.33], t(134) = 21.18, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.08, 95% CI [-1.87, 2.02], t(134) = 0.08, p = 0.939; Std. beta = 0.02, 95% CI [-0.43, 0.47])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.32, 95% CI [-1.41, 0.78], t(134) = -0.56, p = 0.573; Std. beta = -0.07, 95% CI [-0.33, 0.18])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-1.59, 1.68], t(134) = 0.06, p = 0.955; Std. beta = 0.01, 95% CI [-0.37, 0.39])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.81) and the part related to the fixed effects alone (marginal R2) is of 1.75e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.72 (95% CI [2.58, 4.85], t(134) = 6.42, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.27, 95% CI [-1.86, 1.33], t(134) = -0.33, p = 0.742; Std. beta = -0.07, 95% CI [-0.52, 0.37])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.69, 0.80], t(134) = 0.16, p = 0.876; Std. beta = 0.02, 95% CI [-0.19, 0.22])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.07, 95% CI [-1.18, 1.05], t(134) = -0.12, p = 0.906; Std. beta = -0.02, 95% CI [-0.33, 0.29])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 6.27e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.28 (95% CI [2.29, 4.28], t(134) = 6.46, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.46, 95% CI [-1.86, 0.94], t(134) = -0.64, p = 0.522; Std. beta = -0.14, 95% CI [-0.59, 0.30])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.60, 1.03], t(134) = 0.52, p = 0.606; Std. beta = 0.07, 95% CI [-0.19, 0.32])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-1.07, 1.35], t(134) = 0.23, p = 0.817; Std. beta = 0.05, 95% CI [-0.34, 0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.76) and the part related to the fixed effects alone (marginal R2) is of 7.25e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.43 (95% CI [49.08, 53.78], t(134) = 42.92, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -1.23, 95% CI [-4.53, 2.07], t(134) = -0.73, p = 0.466; Std. beta = -0.16, 95% CI [-0.61, 0.28])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.61, 95% CI [-2.33, 1.12], t(134) = -0.69, p = 0.490; Std. beta = -0.08, 95% CI [-0.31, 0.15])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 2.33, 95% CI [-0.24, 4.91], t(134) = 1.77, p = 0.076; Std. beta = 0.31, 95% CI [-0.03, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 3.87e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.39 (95% CI [47.77, 53.02], t(134) = 37.64, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 1.09, 95% CI [-2.60, 4.78], t(134) = 0.58, p = 0.562; Std. beta = 0.13, 95% CI [-0.31, 0.58])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.20, 95% CI [-2.60, 2.20], t(134) = -0.17, p = 0.868; Std. beta = -0.02, 95% CI [-0.31, 0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.73, 95% CI [-4.30, 2.83], t(134) = -0.40, p = 0.686; Std. beta = -0.09, 95% CI [-0.52, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Table